Hyper-Heuristic Algorithm for Urban Traffic Flow Optimization
- Resource Type
- Conference
- Authors
- Hu, Xiao-Min; Duan, Yu-Hui; Li, Min; Zeng, Ying
- Source
- 2023 15th International Conference on Advanced Computational Intelligence (ICACI) Advanced Computational Intelligence (ICACI), 2023 15th International Conference on. :1-7 May, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Roads
Heuristic algorithms
Urban areas
Transportation
Optimization methods
Genetic programming
Traffic flow assignment
hyper heuristic
genetic programming
intelligent transportation
- Language
Traffic flow assignment optimization is a core issue in the field of intelligent transportation. The goal of this problem is to find suitable routes for all travel needs and improve the overall efficiency of the transportation network. This paper proposes a city traffic flow optimization method based on hyper-heuristic. This method uses terminal sets and function sets designed according to the characteristics of urban road networks to construct hyper-heuristic strategies and simulate them on small-scale road networks to test the optimization effects. The hyper-heuristic strategy formulates the current optimal route for each vehicle on the road network and uses Genetic Programming (GP) for iterative training. The average traveling time at the end of each simulation serves as the evaluation value for GP, and finally iteratively outputs the best strategy for simulation and test on larger-scale urban road networks. Tests on different sizes and regions of road networks show that using GP iterative training can improve the traffic efficiency of urban road networks with hyper-heuristic strategies.